modification following review from sbromberger

src/community/assortativity.jl

@@ -1,6 +1,6 @@
 """
     assortativity(g)
-    assortativity(g,attributes)
+    assortativity(g, attributes)
 
 Return the [assortativity coefficient](https://en.wikipedia.org/wiki/Assortativity)
 of graph `g`, defined as the Pearson correlation of excess degree between
@@ -48,9 +48,10 @@ function assortativity(g::AbstractGraph{T}) where T
     return assortativity_coefficient(g, sjk, sj, sk, sjs, sks, nue)
 end
 
-function assortativity(g::AbstractGraph{T},attributes::Vector{N}) where {T,N<:Number}
+function assortativity(g::AbstractGraph{T}, attributes::Vector{N}) where {T,N<:Number}
+    P = promote_type(Int64, N) # at least Int64 to reduce risk of overflow
     nue  = ne(g)
-    sjk = sj = sk = sjs = sks = zero(N)
+    sjk = sj = sk = sjs = sks = zero(P)
     for d in edges(g)
         j = attributes[src(d)]
         k = attributes[dst(d)]

test/community/assortativity.jl

@@ -24,22 +24,22 @@ using Random, Statistics
     # Test definition of assortativity as Pearson correlation coefficient
     # between some scalar attributes characterising each vertex
     @testset "Small graphs with attributes" for n = 5:2:11
-        g = grid([1,n])
-        attributes = [(-1.)^v for v in vertices(g)]
-        @test @inferred assortativity(g,attributes) ≈ -1.
+        g = grid([1, n])
+        attributes = [(-1.)^v for v in 1:nv(g)]
+        @test @inferred assortativity(g, attributes) ≈ -1.
     end
     @testset "Directed ($seed) with attributes" for seed in [1, 2, 3], (_n, _ne) in [(14, 18), (10, 22), (7, 16)]
         g = erdos_renyi(_n, _ne; is_directed=true, seed=seed)
         attributes = randn(_n)
-        assort = assortativity(g,attributes)
+        assort = assortativity(g, attributes)
         x = [attributes[src(d)] for d in edges(g)]
         y = [attributes[dst(d)]  for d in edges(g)]
         @test @inferred assort ≈ cor(x, y)
     end
     @testset "Undirected ($seed) with attributes" for seed in [1, 2, 3], (_n, _ne) in [(14, 18), (10, 22), (7, 16)]
         g = erdos_renyi(_n, _ne; is_directed=false, seed=seed)
         attributes = randn(_n)
-        assort = assortativity(g,attributes)
+        assort = assortativity(g, attributes)
         x = [attributes[src(d)] for d in edges(g)]
         y = [attributes[dst(d)]  for d in edges(g)]
         @test @inferred assort ≈ cor([x;y], [y;x])